Results 11 to 20 of about 2,128 (206)

Neuroevolution

Scholarpedia, 2013
Neuroevolution is a machine learning technique that applies evolutionary algorithms to construct artificial neural networks, taking inspiration from the evolution of biological nervous systems in nature. Compared to other neural network learning methods,
Lehman, Joel, Miikkulainen, Risto
core   +4 more sources

A NEAT Visualisation of Neuroevolution Trajectories [PDF]

, 2021
NeuroEvolution of Augmenting Topologies (NEAT) is a system for evolving neural network topologies along with weights that has proven highly effective and adaptable for solving challenging reinforcement learning tasks. This paper analyses NEAT through the
Ochoa, Gabriela, Sarti, Stefano, Stefano Sarti, Gabriela Ochoa   +3 more
core   +3 more sources

A Study of Fitness Landscapes for Neuroevolution [PDF]

2020 IEEE Congress on Evolutionary Computation (CEC), 2020
Rodrigues, N. M., Silva, S., & Vanneschi, L. (2020). A Study of Fitness Landscapes for Neuroevolution. In 2020 IEEE Congress on Evolutionary Computation, CEC 2020: Conference Proceedings [9185783] (2020 IEEE Congress on Evolutionary Computation, CEC 2020
Sara Silva, Leonardo Vanneschi, Nuno M. Rodrigues, Silva, Sara, Rodrigues, Nuno M., Vanneschi, Leonardo   +5 more
core   +5 more sources

Using neuroevolution for designing soft medical devices

Biomimetic Intelligence and Robotics
Soft robots can exhibit better performance in specific tasks compared to conventional robots, particularly in healthcare related tasks. However, the field of soft robotics is still young, and designing them often involves mimicking natural organisms or ...
Hugo Alcaraz-Herrera, Michail-Antisthenis Tsompanas, Igor Balaz, Andrew Adamatzky   +3 more
doaj   +2 more sources

Momentum Enhanced Neuroevolution

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, 2015
The momentum parameter is common within numerous optimization and local search algorithms, particularly in the popular back propagation neural network learning algorithm.
Sher, Gene, Gene Sher
core   +4 more sources

Control of Biohybrid Actuators using NeuroEvolution

Proceedings of the 16th International Joint Conference on Computational Intelligence
In medical-related tasks, soft robots can perform better than conventional robots because of their compliant building materials and the movements they are able perform.
Alcaraz-Herrera, Hugo, Tsompanas, Michail-Antisthenis, Balaz, Igor, Adamatzky, Andrew   +3 more
core   +3 more sources

Recombination and Novelty in Neuroevolution: A Visual Analysis [PDF]

, 2022
Neuroevolution has re-emerged as an active topic in the last few years. However, there is a lack of accessible tools to analyse, contrast and visualise the behaviour of neuroevolution systems.
Ochoa, Gabriela, Sarti, Stefano, Adair, Jason   +2 more
core   +1 more source

NeuroSCA: Evolving Activation Functions for Side-Channel Analysis

IEEE Access, 2023
The choice of activation functions can significantly impact the performance of neural networks. Due to an ever-increasing number of new activation functions being proposed in the literature, selecting the appropriate activation function becomes even more
Karlo Knezevic, Juraj Fulir, Domagoj Jakobovic, Stjepan Picek, Marko Durasevic   +4 more
doaj   +1 more source

Neuroevolution of self-interpretable agents [PDF]

Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 2020
To appear at the Genetic and Evolutionary Computation Conference (GECCO 2020) as a full ...
Yujin Tang, Duong Nguyen, David Ha
openaire   +3 more sources

Hetero-Dimensional Multitask Neuroevolution for Chaotic Time Series Prediction

IEEE Access, 2020
Chaotic time series prediction has important research and application value, and neural network-based prediction methods have problems such as low accuracy and difficulty in determining the number of nodes in the hidden layer.
Daoqing Zhang, Mingyan Jiang
doaj   +1 more source